Initialize Project
# Uncomment lines below if rmd file is placed in a subdirectory
# library(knitr)
# opts_knit$set(root.dir = normalizePath('../')) Explore
No of app received by # week in year, colored by month
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## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layout## Warning: Ignoring unknown parameters: binwidth, bins, pad## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`No of app rcvd by # of week in year, colored by source code
## Warning: Ignoring unknown parameters: binwidth, bins, pad## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`Recency by week, number of last submit week and number of the week before last submit (current week = 51)
Some EDA on recency
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`## Warning: Removed 592 rows containing non-finite values (stat_bin).Summary by day of week (Mon = 1 .. Sun = 7)
Summary by weekly
EDA on time, submit by day of week and by week
## Warning: Removed 292 rows containing non-finite values (stat_bin).## Warning: Removed 10 rows containing missing values (geom_path).
## Saving 7 x 5 in image## Warning: Removed 292 rows containing non-finite values (stat_bin).
## Warning: Removed 10 rows containing missing values (geom_path).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Saving 7 x 5 in image## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.product, bundle preference
EDA on Contribution by Products / Bundle
## Saving 7 x 5 in image## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Saving 7 x 5 in image
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
combine all featues
## Joining, by = "agent_code"
## Joining, by = "agent_code"
## Joining, by = "agent_code"
## Joining, by = "agent_code"Clustering I
Hierarchical
## clust_group
## 1 2 3 4 5
## 1517 481 722 175 155EDA clustering efficiency
Mapping agent cluster with advisor performance group
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## 1 2 3 4 5
## 1517 481 722 175 155##
## 1 2 3 4 5
## 1496 481 708 161 155Analysis I
Composition of sales type (cluster) for high performance advisor
## 1 2 3 4 5
## A 40.37267 14.90683 31.67702 5.590062 7.4534161
## A+ 39.76510 28.18792 19.46309 4.865772 7.7181208
## B 48.17391 15.30435 22.78261 5.739130 8.0000000
## C 55.34351 12.97710 23.28244 2.671756 5.7251908
## D 55.61139 11.72529 24.45561 7.370184 0.8375209
## DM 61.79775 11.23596 20.67416 3.820225 2.4719101
## Pro 18.10345 19.82759 58.62069 1.724138 1.7241379
As mosaic plot shown the different contribution sales type in each advisor group A+ .. B : Have ‘type 5 - high , switching’ more than other group. A+ : Have ‘type 2 - moderate, continue’. Pro : Mostly contributed by ‘type 3 - New active’ since mostly is the new sales, mostly bundle DM : Interesting contributed most the ‘type 1 - little, dormant’
Clustering II
K-Means clustering
